"""simple docstring"""



import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


SCREAMING_SNAKE_CASE__:Optional[int]									=			pytest.mark.integration






@require_faiss
class  snake_case__      (       snake_case_       ):


 def 						a__   ( self						):
        __a       =    Dataset.from_dict({"filename": ["my_name-train" + "_" + str(lowerCamelCase						) for x in np.arange(30						).tolist()]}						)
        return dset


 def 						a__   ( self						):
        import faiss

        __a       =    self._create_dummy_dataset()
        __a       =    dset.map(
            lambda lowerCamelCase    ,		lowerCamelCase						: {"vecs": i * np.ones(5    ,		dtype=np.floataa						)}    ,		with_indices=lowerCamelCase    ,		keep_in_memory=lowerCamelCase						)
        __a       =    dset.add_faiss_index("vecs"    ,		batch_size=100    ,		metric_type=faiss.METRIC_INNER_PRODUCT						)
        __a ,   __a       =    dset.get_nearest_examples("vecs"    ,		np.ones(5    ,		dtype=np.floataa						)						)
        self.assertEqual(examples["filename"][0]    ,		"my_name-train_29"						)
        dset.drop_index("vecs"						)


 def 						a__   ( self						):
        import faiss

        __a       =    self._create_dummy_dataset()
        dset.add_faiss_index_from_external_arrays(
            external_arrays=np.ones((30, 5)						) * np.arange(30						).reshape(-1    ,		1						)    ,		index_name="vecs"    ,		batch_size=100    ,		metric_type=faiss.METRIC_INNER_PRODUCT    ,		)
        __a ,   __a       =    dset.get_nearest_examples("vecs"    ,		np.ones(5    ,		dtype=np.floataa						)						)
        self.assertEqual(examples["filename"][0]    ,		"my_name-train_29"						)


 def 						a__   ( self						):
        import faiss

        __a       =    self._create_dummy_dataset()
        dset.add_faiss_index_from_external_arrays(
            external_arrays=np.ones((30, 5)						) * np.arange(30						).reshape(-1    ,		1						)    ,		index_name="vecs"    ,		metric_type=faiss.METRIC_INNER_PRODUCT    ,		)

        # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
        # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
        # see https://bugs.python.org/issue14243 and
        # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
        with tempfile.NamedTemporaryFile(delete=lowerCamelCase						) as tmp_file:
               dset.save_faiss_index("vecs"    ,		tmp_file.name						)
               dset.load_faiss_index("vecs2"    ,		tmp_file.name						)
        os.unlink(tmp_file.name						)

        __a ,   __a       =    dset.get_nearest_examples("vecs2"    ,		np.ones(5    ,		dtype=np.floataa						)						)
        self.assertEqual(examples["filename"][0]    ,		"my_name-train_29"						)


 def 						a__   ( self						):
        __a       =    self._create_dummy_dataset()
        dset.add_faiss_index_from_external_arrays(
            external_arrays=np.ones((30, 5)						) * np.arange(30						).reshape(-1    ,		1						)    ,		index_name="vecs"						)
        dset.drop_index("vecs"						)
        self.assertRaises(lowerCamelCase    ,		partial(dset.get_nearest_examples    ,		"vecs2"    ,		np.ones(5    ,		dtype=np.floataa						)						)						)






 def 						a__   ( self						):
        from elasticsearch import Elasticsearch

        __a       =    self._create_dummy_dataset()
        with patch("elasticsearch.Elasticsearch.search"						) as mocked_search, patch(
            "elasticsearch.client.IndicesClient.create"						) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"						) as mocked_bulk:
               __a       =    {"acknowledged": True}
               mocked_bulk.return_value([(True, None)] * 30						)
               __a       =    {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
               __a       =    Elasticsearch()

               dset.add_elasticsearch_index("filename"    ,		es_client=lowerCamelCase						)
               __a ,   __a       =    dset.get_nearest_examples("filename"    ,		"my_name-train_29"						)
               self.assertEqual(examples["filename"][0]    ,		"my_name-train_29"						)






@require_faiss
class  snake_case__      (       snake_case_       ):


 def 						a__   ( self						):
        import faiss

        __a       =    FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT						)

        # add vectors
        index.add_vectors(np.eye(5    ,		dtype=np.floataa						)						)
        self.assertIsNotNone(index.faiss_index						)
        self.assertEqual(index.faiss_index.ntotal    ,		5						)
        index.add_vectors(np.zeros((5, 5)    ,		dtype=np.floataa						)						)
        self.assertEqual(index.faiss_index.ntotal    ,		10						)

        # single query
        __a       =    np.zeros(5    ,		dtype=np.floataa						)
        __a       =    1
        __a ,   __a       =    index.search(lowerCamelCase						)
        self.assertRaises(lowerCamelCase    ,		index.search    ,		query.reshape(-1    ,		1						)						)
        self.assertGreater(scores[0]    ,		0						)
        self.assertEqual(indices[0]    ,		1						)

        # batched queries
        __a       =    np.eye(5    ,		dtype=np.floataa						)[::-1]
        __a ,   __a       =    index.search_batch(lowerCamelCase						)
        self.assertRaises(lowerCamelCase    ,		index.search_batch    ,		queries[0]						)
        __a       =    [scores[0] for scores in total_scores]
        __a       =    [indices[0] for indices in total_indices]
        self.assertGreater(np.min(lowerCamelCase						)    ,		0						)
        self.assertListEqual([4, 3, 2, 1, 0]    ,		lowerCamelCase						)


 def 						a__   ( self						):
        import faiss

        __a       =    FaissIndex(string_factory="Flat"						)
        index.add_vectors(np.eye(5    ,		dtype=np.floataa						)						)
        self.assertIsInstance(index.faiss_index    ,		faiss.IndexFlat						)
        __a       =    FaissIndex(string_factory="LSH"						)
        index.add_vectors(np.eye(5    ,		dtype=np.floataa						)						)
        self.assertIsInstance(index.faiss_index    ,		faiss.IndexLSH						)
        with self.assertRaises(lowerCamelCase						):
               __a       =    FaissIndex(string_factory="Flat"    ,		custom_index=faiss.IndexFlat(5						)						)


 def 						a__   ( self						):
        import faiss

        __a       =    faiss.IndexFlat(5						)
        __a       =    FaissIndex(custom_index=lowerCamelCase						)
        index.add_vectors(np.eye(5    ,		dtype=np.floataa						)						)
        self.assertIsInstance(index.faiss_index    ,		faiss.IndexFlat						)






 def 						a__   ( self						):
        import faiss

        __a       =    FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT						)
        index.add_vectors(np.eye(5    ,		dtype=np.floataa						)						)

        # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
        # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
        # see https://bugs.python.org/issue14243 and
        # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
        with tempfile.NamedTemporaryFile(delete=lowerCamelCase						) as tmp_file:
               index.save(tmp_file.name						)
               __a       =    FaissIndex.load(tmp_file.name						)
        os.unlink(tmp_file.name						)

        __a       =    np.zeros(5    ,		dtype=np.floataa						)
        __a       =    1
        __a ,   __a       =    index.search(lowerCamelCase						)
        self.assertGreater(scores[0]    ,		0						)
        self.assertEqual(indices[0]    ,		1						)



@require_faiss
def 	_lowerCamelCase(	a				):
       import faiss

       __a       =    FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT				)
       index.add_vectors(np.eye(5   ,    dtype=np.floataa				)				)

       __a       =    "index.faiss"
       __a       =    F"mock://{index_name}"
       index.save(a   ,    storage_options=mockfs.storage_options				)
       __a       =    FaissIndex.load(a   ,    storage_options=mockfs.storage_options				)

       __a       =    np.zeros(5   ,    dtype=np.floataa				)
       __a       =    1
       __a ,   __a       =    index.search(a				)
       assert scores[0] > 0
       assert indices[0] == 1






@require_elasticsearch
class  snake_case__      (       snake_case_       ):


 def 						a__   ( self						):
        from elasticsearch import Elasticsearch

        with patch("elasticsearch.Elasticsearch.search"						) as mocked_search, patch(
            "elasticsearch.client.IndicesClient.create"						) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"						) as mocked_bulk:
               __a       =    Elasticsearch()
               __a       =    {"acknowledged": True}
               __a       =    ElasticSearchIndex(es_client=lowerCamelCase						)
               mocked_bulk.return_value([(True, None)] * 3						)
               index.add_documents(["foo", "bar", "foobar"]						)

               # single query
               __a       =    "foo"
               __a       =    {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
               __a ,   __a       =    index.search(lowerCamelCase						)
               self.assertEqual(scores[0]    ,		1						)
               self.assertEqual(indices[0]    ,		0						)

               # single query with timeout
               __a       =    "foo"
               __a       =    {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
               __a ,   __a       =    index.search(lowerCamelCase    ,		request_timeout=30						)
               self.assertEqual(scores[0]    ,		1						)
               self.assertEqual(indices[0]    ,		0						)

               # batched queries
               __a       =    ["foo", "bar", "foobar"]
               __a       =    {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
               __a ,   __a       =    index.search_batch(lowerCamelCase						)
               __a       =    [scores[0] for scores in total_scores]
               __a       =    [indices[0] for indices in total_indices]
               self.assertGreater(np.min(lowerCamelCase						)    ,		0						)
               self.assertListEqual([1, 1, 1]    ,		lowerCamelCase						)

               # batched queries with timeout
               __a       =    ["foo", "bar", "foobar"]
               __a       =    {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
               __a ,   __a       =    index.search_batch(lowerCamelCase    ,		request_timeout=30						)
               __a       =    [scores[0] for scores in total_scores]
               __a       =    [indices[0] for indices in total_indices]
               self.assertGreater(np.min(lowerCamelCase						)    ,		0						)
               self.assertListEqual([1, 1, 1]    ,		lowerCamelCase						)

"""simple docstring"""



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    0
       __a       =    len(a				) - 1

       while left <= right:
              # avoid divided by 0 during interpolation
              if sorted_collection[left] == sorted_collection[right]:
                     if sorted_collection[left] == item:
                            return left
                     else:
                            return None

              __a       =    left + ((item - sorted_collection[left]) * (right - left)) // (
                  sorted_collection[right] - sorted_collection[left]
              )

              # out of range check
              if point < 0 or point >= len(a				):
                     return None

              __a       =    sorted_collection[point]
              if current_item == item:
                     return point
              else:
                     if point < left:
                            __a       =    left
                            __a       =    point
                     elif point > right:
                            __a       =    right
                            __a       =    point
                     else:
                            if item < current_item:
                                   __a       =    point - 1
                            else:
                                   __a       =    point + 1
       return None



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a				):

       # avoid divided by 0 during interpolation
       if sorted_collection[left] == sorted_collection[right]:
              if sorted_collection[left] == item:
                     return left
              else:
                     return None

       __a       =    left + ((item - sorted_collection[left]) * (right - left)) // (
           sorted_collection[right] - sorted_collection[left]
       )

       # out of range check
       if point < 0 or point >= len(a				):
              return None

       if sorted_collection[point] == item:
              return point
       elif point < left:
              return interpolation_search_by_recursion(a   ,    a   ,    a   ,    a				)
       elif point > right:
              return interpolation_search_by_recursion(a   ,    a   ,    a   ,    a				)
       else:
              if sorted_collection[point] > item:
                     return interpolation_search_by_recursion(
                         a   ,    a   ,    a   ,    point - 1				)
              else:
                     return interpolation_search_by_recursion(
                         a   ,    a   ,    point + 1   ,    a				)



def 	_lowerCamelCase(	a				):
       if collection != sorted(a				):
              raise ValueError("Collection must be ascending sorted"				)
       return True


if __name__ == "__main__":
   import sys
   SCREAMING_SNAKE_CASE__:List[str]									=			0
   if debug == 1:
      SCREAMING_SNAKE_CASE__:Tuple									=			[10, 30, 40, 45, 50, 66, 77, 93]
      try:
         __assert_sorted(collection)
      except ValueError:
         sys.exit("""Sequence must be ascending sorted to apply interpolation search""")
      SCREAMING_SNAKE_CASE__:int									=			67

   SCREAMING_SNAKE_CASE__:List[str]									=			interpolation_search(collection, target)
   if result is not None:
      print(F'''{target} found at positions: {result}''')
   else:
      print("""Not found""")
